Named Entity Recognition (NER): Theory and Implementation
├── Introduction
│   └── Understanding NER
├── Importance of NER
│   ├── In Text Analysis
│   └── In Real-World Applications
├── Designing NER Systems
│   ├── Defining Named Entities
│   └── NER Techniques
├── Implementing NER
│   ├── Data Preparation
│   ├── Model Selection
│   └── Training and Evaluation
├── Practical Example
│   ├── Setup
│   ├── Code Implementation
│   └── Results Analysis
└── Conclusion
    └── Future Directions

Introduction

Understanding NER

• NER is a process in natural language processing (NLP) that identifies named entities in text. These include names of people, organizations, locations, expressions of times, quantities, monetary values, and more.

Importance of NER

In Text Analysis

• NER helps in understanding the key elements of the text, making it easier to organize and categorize the content.

In Real-World Applications

• It's widely used in various applications like information extraction, content classification, customer support systems, and more.

Designing NER Systems

Defining Named Entities

• Identify what constitutes a named entity in your specific domain. This can vary greatly depending on the application and the type of text being analyzed.

NER Techniques

• Techniques for NER include rule-based approaches, machine learning, and deep learning methods.

Implementing NER

Data Preparation

• Gather and preprocess your text data. This includes tokenization, part-of-speech tagging, and sometimes entity annotation.

Model Selection